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Buletin Ilmiah Sarjana Teknik Elektro
Published by Universitas Ahmad Dahlan
ISSN : 26857936     EISSN : 26859572     DOI : 10.12928
Core Subject : Engineering,
Electrical & Electronics Engineering
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
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Articles 3 Documents
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Search results for , issue "Vol. 8 No. 2 (2026): April" : 3 Documents clear
A Self-Balancing 13-Level Single-Phase Triple Gain Inverter Balakrishnan, Sakthisudhursun; Srinivasan, Muralidharan; Subramaniam, Sundaramahalingam; Narayanaswamy, Vanaja
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.14920

Abstract

A potential single stage power electronics interface for integrating renewable sources like PV, fuel cells, etc. with an AC load is a switched capacitor based multilevel inverter with boosting capability. In this research, a thirteen-level MLI topology with voltage boosting factor of three a gain of three for the renewable energy integration is proposed. The proposed MLI requires twelve unidirectional switches, one bidirectional switch, three capacitors, and a single DC source. The voltage stress across each switch is lower than the peak output voltage since the proposed inverter doesn't need a back-end H-Bridge. The proper selection of switching sequence enables the self regulation of voltage across all three capacitors, is self-regulated eliminating the need of additional sensor/control. Simulation results obtained from MATLAB/Simulink confirm the stable operation of the MLI and the self-regulation of switched capacitor voltages under step variations in load, source voltage, and modulation index. A comprehensive comparison with existing topologies demonstrates the superiority of the proposed topology in terms of reduced total number of components and lower total blocking voltage.
Indirect Matrix Converter Based Synchronous Reluctance Motor Drive Systems using Model Predictive Control Laksmi B., Nur Vidia; Mubarok, Muhammad Syahril; Aribowo, Widi; Purwanto, Didik; Isaac, Jacob Raglend; Abdullayev, Vugar Hacimahmud
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15222

Abstract

This paper proposes a speed control strategy of Synchronous Reluctance Motors (SynRM) using an Indirect Matrix Converter (IMC) combined with a finite model predictive speed control (MPSC) and PI current control. This control algoritm is chosen than fully PI in both loops due to improve overall system stability and dynamic response. The IMC architecture offers advantages such as compactness, bidirectional power flow, and the elimination of bulky passive components, making it ideal for efficient motor drive systems. The proposed control method employs predictive algorithm using augmented state variable and cost function minimization technique. In addition, PI controllers here using a pole-assignment method. Both proposed controls aim to guarantee stability and responsiveness for dynamic performances. The MATLAB/Simulink is used here to simulate the system, incorporating practical motor parameters and space vector modulation techniques. Simulation results show that the control algorithm attains satisfactory speed performance, with minimal steady-state error 0.47%, overshoot below 2%, and fast settling time under various load 0.035 seconds and speed profiles. Additionally, the system performs robustly under reversed and sinusoidal speed commands, demonstrating its effectiveness and suitability for real-world industrial applications also need to implement in the experiment for the future works.
Language as the Semantic Bridge in Audio, Music, and Multimodal Artificial Intelligence: A Systematic Review (2021-2025) Ratnasari, Novia; Wibawa, Aji Prasetya
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15564

Abstract

This study presents a systematic review of research in Audio, Music, and Multimodal Artificial Intelligence published between 2021 and 2025, investigating how language operates as a semantic mediation layer between acoustic signals and high-level meaning. The research addresses the fragmentation of existing surveys by introducing a Domain; Modality; Technique; Task (D-M-T-T) taxonomy that systematically differentiates domain focus, modality configuration, modeling techniques, and task objectives. The research contribution is a structured analytical framework that offers a more granular perspective than architecture-centered surveys of Multimodal Large Language Models. Following the PRISMA 2020 protocol, 2,197 Scopus-indexed publications were screened, yielding 369 eligible studies. Language is defined as a representational layer encompassing natural language and structured symbolic encodings that connect acoustic embeddings to semantic interpretation and generative reasoning. Multimodal systems aligning audio and vision without explicit textual grounding are included and analyzed as non-linguistic alignment architectures within the taxonomy. The findings reveal a shift from recognition-based models toward unified multimodal systems in which language conditions alignment, reasoning, and generative synthesis. For instance, text-conditioned music generation demonstrates how linguistic prompts guide compositional structure and emotional expression. These developments reflect an epistemic transition from signal recognition paradigms to language-mediated generative intelligence. Emerging gaps include limited explainability in generative audio systems and insufficient low-resource cross-modal semantic grounding.

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